org.apache.sysds.hops

## Class OptimizerUtils

- java.lang.Object
- - org.apache.sysds.hops.OptimizerUtils

-     public class OptimizerUtils
      extends Object

- - ### Nested Class Summary

    Nested Classes
    Modifier and Type | Class and Description
    --- | ---
    `static class` | `OptimizerUtils.OptimizationLevel` Optimization Types for Compilation O0 STATIC - Decisions for scheduling operations on CP/MR are based on predefined set of rules, which check if the dimensions are below a fixed/static threshold (OLD Method of choosing between CP and MR).

  - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static boolean` | `ALLOW_ALGEBRAIC_SIMPLIFICATION`
    `static boolean` | `ALLOW_AUTO_VECTORIZATION`
    `static boolean` | `ALLOW_BRANCH_REMOVAL` Enables if-else branch removal for constant predicates (original literals or results of constant folding).
    `static boolean` | `ALLOW_CODE_MOTION` Enables a specific rewrite for code motion, i.e., hoisting loop invariant code out of while, for, and parfor loops.
    `static boolean` | `ALLOW_COMBINE_FILE_INPUT_FORMAT` Enables the use of CombineSequenceFileInputFormat with splitsize = 2x hdfs blocksize, if sort buffer size large enough and parallelism not hurt.
    `static boolean` | `ALLOW_COMMON_SUBEXPRESSION_ELIMINATION` Enables common subexpression elimination in dags.
    `static boolean` | `ALLOW_COMPRESSION_REWRITE` Boolean specifying if compression rewrites is allowed.
    `static boolean` | `ALLOW_CONSTANT_FOLDING` Enables constant folding in dags.
    `static boolean` | `ALLOW_FOR_LOOP_REMOVAL` Enables the removal of (par)for-loops when from, to, and increment are constants (original literals or results of constant folding) and lead to an empty sequence, i.e., (par)for-loops without a single iteration.
    `static boolean` | `ALLOW_INTER_PROCEDURAL_ANALYSIS` Enables interprocedural analysis between main script and functions as well as functions and other functions.
    `static boolean` | `ALLOW_LOOP_UPDATE_IN_PLACE` Enables a specific rewrite that enables update in place for loop variables that are only read/updated via cp leftindexing.
    `static boolean` | `ALLOW_OPERATOR_FUSION`
    `static boolean` | `ALLOW_RAND_JOB_RECOMPILE`
    `static boolean` | `ALLOW_RUNTIME_PIGGYBACKING` Enables parfor runtime piggybacking of MR jobs into the packed jobs for scan sharing.
    `static boolean` | `ALLOW_SCRIPT_LEVEL_COMPRESS_COMMAND` This variable allows for insertion of Compress and decompress in the dml script from the user.
    `static boolean` | `ALLOW_SIZE_EXPRESSION_EVALUATION` Enables simple expression evaluation for datagen parameters 'rows', 'cols'.
    `static boolean` | `ALLOW_SPLIT_HOP_DAGS` Enables a specific hop dag rewrite that splits hop dags after csv persistent reads with unknown size in order to allow for recompile.
    `static boolean` | `ALLOW_SUM_PRODUCT_REWRITES` Enables sum product rewrites such as mapmultchains.
    `static boolean` | `ALLOW_TRANSITIVE_SPARK_EXEC_TYPE` Enable transitive spark execution type selection.
    `static boolean` | `ALLOW_WORSTCASE_SIZE_EXPRESSION_EVALUATION` Enables simple expression evaluation for datagen parameters 'rows', 'cols'.
    `static boolean` | `ASYNC_TRIGGER_RDD_OPERATIONS` Enable prefetch and broadcast.
    `static long` | `BOOLEAN_SIZE`
    `static long` | `CHAR_SIZE`
    `static int` | `DEFAULT_BLOCKSIZE` Default blocksize if unspecified or for testing purposes
    `static int` | `DEFAULT_FRAME_BLOCKSIZE` Default frame blocksize
    `static OptimizerUtils.OptimizationLevel` | `DEFAULT_OPTLEVEL` Default optimization level if unspecified
    `static double` | `DEFAULT_SIZE` Default memory size, which is used if the actual estimate can not be computed e.g., when input/output dimensions are unknown.
    `static long` | `DOUBLE_SIZE`
    `static boolean` | `FEDERATED_COMPILATION` Compile federated instructions based on input federation state and privacy constraints.
    `static long` | `INT_SIZE`
    `static double` | `INVALID_SIZE`
    `static int` | `IPA_NUM_REPETITIONS` Number of inter-procedural analysis (IPA) repetitions.
    `static long` | `MAX_NNZ_CP_SPARSE`
    `static long` | `MAX_NUMCELLS_CP_DENSE`
    `static double` | `MEM_UTIL_FACTOR` Utilization factor used in deciding whether an operation to be scheduled on CP or MR.
    `static double` | `PARALLEL_CP_READ_PARALLELISM_MULTIPLIER` Specifies a multiplier computing the degree of parallelism of parallel text read/write out of the available degree of parallelism.
    `static double` | `PARALLEL_CP_WRITE_PARALLELISM_MULTIPLIER`
    `static long` | `SAFE_REP_CHANGE_THRES`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `OptimizerUtils()`

  - ### Method Summary

    All Methods Static Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `static boolean` | `allowsToFilterEmptyBlockOutputs(Hop hop)`
    `static boolean` | `checkSparkBroadcastMemoryBudget(double size)`
    `static boolean` | `checkSparkBroadcastMemoryBudget(long rlen, long clen, long blen, long nnz)`
    `static boolean` | `checkSparkCollectMemoryBudget(DataCharacteristics dc, long memPinned)`
    `static boolean` | `checkSparkCollectMemoryBudget(DataCharacteristics dc, long memPinned, boolean checkBP)`
    `static boolean` | `checkSparseBlockCSRConversion(DataCharacteristics dcIn)`
    `static CompilerConfig` | `constructCompilerConfig(CompilerConfig cconf, DMLConfig dmlconf)`
    `static CompilerConfig` | `constructCompilerConfig(DMLConfig dmlconf)`
    `static long` | `estimatePartitionedSizeExactSparsity(DataCharacteristics dc)` Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with the given matrix characteristics
    `static long` | `estimatePartitionedSizeExactSparsity(DataCharacteristics dc, boolean outputEmptyBlocks)`
    `static long` | `estimatePartitionedSizeExactSparsity(Hop hop)` Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with the hops dimensions and number of non-zeros nnz.
    `static long` | `estimatePartitionedSizeExactSparsity(long rlen, long clen, long blen, double sp)` Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with dimensions=(nrows,ncols) and sparsity=sp.
    `static long` | `estimatePartitionedSizeExactSparsity(long rlen, long clen, long blen, double sp, boolean outputEmptyBlocks)`
    `static long` | `estimatePartitionedSizeExactSparsity(long rlen, long clen, long blen, long nnz)` Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with dimensions=(nrows,ncols) and number of non-zeros nnz.
    `static long` | `estimatePartitionedSizeExactSparsity(long rlen, long clen, long blen, long nnz, boolean outputEmptyBlocks)`
    `static long` | `estimateSize(DataCharacteristics dc)`
    `static long` | `estimateSize(long nrows, long ncols)` Similar to estimate() except that it provides worst-case estimates when the optimization type is ROBUST.
    `static long` | `estimateSizeEmptyBlock(long nrows, long ncols)`
    `static long` | `estimateSizeExactSparsity(DataCharacteristics dc)`
    `static long` | `estimateSizeExactSparsity(long nrows, long ncols, double sp)` Estimates the footprint (in bytes) for an in-memory representation of a matrix with dimensions=(nrows,ncols) and sparsity=sp.
    `static long` | `estimateSizeExactSparsity(long nrows, long ncols, long nnz)` Estimates the footprint (in bytes) for an in-memory representation of a matrix with dimensions=(nrows,ncols) and and number of non-zeros nnz.
    `static long` | `estimateSizeTextOutput(int[] dims, long nnz, Types.FileFormat fmt)`
    `static long` | `estimateSizeTextOutput(long rows, long cols, long nnz, Types.FileFormat fmt)`
    `static boolean` | `exceedsCachingThreshold(long dim2, double outMem)` Indicates if the given matrix characteristics exceed the threshold for caching, i.e., the matrix should be cached.
    `static double` | `getBinaryOpSparsity(double sp1, double sp2, Types.OpOp2 op, boolean worstcase)` Estimates the result sparsity for matrix-matrix binary operations (A op B)
    `static double` | `getBinaryOpSparsityConditionalSparseSafe(double sp1, Types.OpOp2 op, LiteralOp lit)`
    `static int` | `getConstrainedNumThreads(int maxNumThreads)`
    `static Types.ExecMode` | `getDefaultExecutionMode()`
    `static int` | `getDefaultFrameSize()`
    `static org.apache.log4j.Level` | `getDefaultLogLevel()`
    `static long` | `getDefaultSize()`
    `static double` | `getLeftIndexingSparsity(long rlen1, long clen1, long nnz1, long rlen2, long clen2, long nnz2)`
    `static double` | `getLocalMemBudget()` Returns memory budget (according to util factor) in bytes
    `static long` | `getMatMultNnz(double sp1, double sp2, long m, long k, long n, boolean worstcase)`
    `static double` | `getMatMultSparsity(double sp1, double sp2, long m, long k, long n, boolean worstcase)` Estimates the result sparsity for Matrix Multiplication A %*% B.
    `static long` | `getNnz(long dim1, long dim2, double sp)`
    `static long` | `getNumIterations(ForProgramBlock fpb, LocalVariableMap vars, long defaultValue)`
    `static long` | `getNumIterations(ForProgramBlock fpb, long defaultValue)`
    `static int` | `getNumMappers()`
    `static int` | `getNumReducers(boolean configOnly)` Returns the number of reducers that potentially run in parallel.
    `static OptimizerUtils.OptimizationLevel` | `getOptLevel()`
    `static long` | `getOuterNonZeros(long n1, long n2, long nnz1, long nnz2, Types.OpOp2 op)`
    `static int` | `getParallelBinaryReadParallelism()`
    `static int` | `getParallelBinaryWriteParallelism()`
    `static int` | `getParallelTextReadParallelism()` Returns the degree of parallelism used for parallel text read.
    `static int` | `getParallelTextWriteParallelism()` Returns the degree of parallelism used for parallel text write.
    `static double` | `getSparsity(DataCharacteristics dc)`
    `static double` | `getSparsity(Hop hop)`
    `static double` | `getSparsity(long[] dims, long nnz)`
    `static double` | `getSparsity(long dim1, long dim2, long nnz)`
    `static double` | `getTotalMemEstimate(Hop[] in, Hop out)`
    `static double` | `getTotalMemEstimate(Hop[] in, Hop out, boolean denseOut)`
    `static String` | `getUniqueTempFileName()` Wrapper over internal filename construction for external usage.
    `static boolean` | `isBinaryOpConditionalSparseSafe(Types.OpOp2 op)` Determines if a given binary op is potentially conditional sparse safe.
    `static boolean` | `isBinaryOpConditionalSparseSafeExact(Types.OpOp2 op, LiteralOp lit)` Determines if a given binary op with scalar literal guarantee an output sparsity which is exactly the same as its matrix input sparsity.
    `static boolean` | `isBinaryOpSparsityConditionalSparseSafe(Types.OpOp2 op, LiteralOp lit)`
    `static boolean` | `isHybridExecutionMode()`
    `static boolean` | `isIndexingRangeBlockAligned(IndexRange ixrange, DataCharacteristics mc)` Indicates if the given indexing range is block aligned, i.e., it does not require global aggregation of blocks.
    `static boolean` | `isIndexingRangeBlockAligned(long rl, long ru, long cl, long cu, long blen)` Indicates if the given indexing range is block aligned, i.e., it does not require global aggregation of blocks.
    `static boolean` | `isMaxLocalParallelism(int k)`
    `static boolean` | `isMemoryBasedOptLevel()`
    `static boolean` | `isOptLevel(OptimizerUtils.OptimizationLevel level)`
    `static boolean` | `isSparkExecutionMode()`
    `static boolean` | `isTopLevelParFor()`
    `static boolean` | `isValidCPDimensions(DataCharacteristics mc)`
    `static boolean` | `isValidCPDimensions(long rows, long cols)` Returns false if dimensions known to be invalid; other true
    `static boolean` | `isValidCPDimensions(Types.ValueType[] schema, String[] names)` Returns false if schema and names are not properly specified; other true Length to be > 0, and length of both to be equal.
    `static boolean` | `isValidCPMatrixSize(long rows, long cols, double sparsity)` Determines if valid matrix size to be represented in CP data structures.
    `static void` | `resetDefaultSize()`
    `static void` | `resetStaticCompilerFlags()`
    `static double` | `rEvalSimpleDoubleExpression(Hop root, HashMap<Long,Double> valMemo)`
    `static double` | `rEvalSimpleDoubleExpression(Hop root, HashMap<Long,Double> valMemo, LocalVariableMap vars)`
    `static long` | `rEvalSimpleLongExpression(Hop root, HashMap<Long,Long> valMemo)` Function to evaluate simple size expressions over literals and now/ncol.
    `static long` | `rEvalSimpleLongExpression(Hop root, HashMap<Long,Long> valMemo, LocalVariableMap vars)`
    `static String` | `toMB(double inB)`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### MEM_UTIL_FACTOR

          public static double MEM_UTIL_FACTOR

      Utilization factor used in deciding whether an operation to be scheduled on CP or MR. NOTE: it is important that MEM_UTIL_FACTOR+CacheableData.CACHING_BUFFER_SIZE < 1.0

    - #### DEFAULT_BLOCKSIZE

          public static final int DEFAULT_BLOCKSIZE

      Default blocksize if unspecified or for testing purposes
      See Also:
        Constant Field Values

    - #### DEFAULT_FRAME_BLOCKSIZE

          public static final int DEFAULT_FRAME_BLOCKSIZE

      Default frame blocksize
      See Also:
        Constant Field Values

    - #### DEFAULT_OPTLEVEL

          public static final OptimizerUtils.OptimizationLevel DEFAULT_OPTLEVEL

      Default optimization level if unspecified

    - #### DEFAULT_SIZE

          public static double DEFAULT_SIZE

      Default memory size, which is used if the actual estimate can not be computed e.g., when input/output dimensions are unknown. The default is set to a large value so that operations are scheduled on MR while avoiding overflows as well.

    - #### DOUBLE_SIZE

          public static final long DOUBLE_SIZE

      See Also:
        Constant Field Values

    - #### INT_SIZE

          public static final long INT_SIZE

      See Also:
        Constant Field Values

    - #### CHAR_SIZE

          public static final long CHAR_SIZE

      See Also:
        Constant Field Values

    - #### BOOLEAN_SIZE

          public static final long BOOLEAN_SIZE

      See Also:
        Constant Field Values

    - #### INVALID_SIZE

          public static final double INVALID_SIZE

      See Also:
        Constant Field Values

    - #### MAX_NUMCELLS_CP_DENSE

          public static final long MAX_NUMCELLS_CP_DENSE

      See Also:
        Constant Field Values

    - #### MAX_NNZ_CP_SPARSE

          public static final long MAX_NNZ_CP_SPARSE

    - #### SAFE_REP_CHANGE_THRES

          public static final long SAFE_REP_CHANGE_THRES

      See Also:
        Constant Field Values

    - #### ALLOW_COMMON_SUBEXPRESSION_ELIMINATION

          public static boolean ALLOW_COMMON_SUBEXPRESSION_ELIMINATION

      Enables common subexpression elimination in dags. There is however, a potential tradeoff between computation redundancy and data transfer between MR jobs. Since, we do not reason about transferred data yet, this rewrite rule is enabled by default.

    - #### ALLOW_CONSTANT_FOLDING

          public static boolean ALLOW_CONSTANT_FOLDING

      Enables constant folding in dags. Constant folding computes simple expressions of binary operations and literals and replaces the hop sub-DAG with a new literal operator.

    - #### ALLOW_ALGEBRAIC_SIMPLIFICATION

          public static boolean ALLOW_ALGEBRAIC_SIMPLIFICATION

    - #### ALLOW_OPERATOR_FUSION

          public static boolean ALLOW_OPERATOR_FUSION

    - #### ALLOW_BRANCH_REMOVAL

          public static boolean ALLOW_BRANCH_REMOVAL

      Enables if-else branch removal for constant predicates (original literals or results of constant folding).

    - #### ALLOW_FOR_LOOP_REMOVAL

          public static boolean ALLOW_FOR_LOOP_REMOVAL

      Enables the removal of (par)for-loops when from, to, and increment are constants (original literals or results of constant folding) and lead to an empty sequence, i.e., (par)for-loops without a single iteration.

    - #### ALLOW_AUTO_VECTORIZATION

          public static boolean ALLOW_AUTO_VECTORIZATION

    - #### ALLOW_SIZE_EXPRESSION_EVALUATION

          public static boolean ALLOW_SIZE_EXPRESSION_EVALUATION

      Enables simple expression evaluation for datagen parameters 'rows', 'cols'. Simple expressions are defined as binary operations on literals and nrow/ncol. This applies only to exact size information.

    - #### ALLOW_WORSTCASE_SIZE_EXPRESSION_EVALUATION

          public static boolean ALLOW_WORSTCASE_SIZE_EXPRESSION_EVALUATION

      Enables simple expression evaluation for datagen parameters 'rows', 'cols'. Simple expressions are defined as binary operations on literals and b(+) or b(*) on nrow/ncol. This applies also to worst-case size information.

    - #### ALLOW_RAND_JOB_RECOMPILE

          public static boolean ALLOW_RAND_JOB_RECOMPILE

    - #### ALLOW_RUNTIME_PIGGYBACKING

          public static boolean ALLOW_RUNTIME_PIGGYBACKING

      Enables parfor runtime piggybacking of MR jobs into the packed jobs for scan sharing.

    - #### ALLOW_INTER_PROCEDURAL_ANALYSIS

          public static boolean ALLOW_INTER_PROCEDURAL_ANALYSIS

      Enables interprocedural analysis between main script and functions as well as functions and other functions. This includes, for example, to propagate statistics into functions if save to do so (e.g., if called once).

    - #### IPA_NUM_REPETITIONS

          public static int IPA_NUM_REPETITIONS

      Number of inter-procedural analysis (IPA) repetitions. If set to >=2, we apply IPA multiple times in order to allow scalar propagation over complex function call graphs and various interactions between constant propagation, constant folding, and other rewrites such as branch removal and the merge of statement block sequences.

    - #### ALLOW_SUM_PRODUCT_REWRITES

          public static boolean ALLOW_SUM_PRODUCT_REWRITES

      Enables sum product rewrites such as mapmultchains. In the future, this will cover all sum-product related rewrites.

    - #### ALLOW_SPLIT_HOP_DAGS

          public static boolean ALLOW_SPLIT_HOP_DAGS

      Enables a specific hop dag rewrite that splits hop dags after csv persistent reads with unknown size in order to allow for recompile.

    - #### ALLOW_LOOP_UPDATE_IN_PLACE

          public static boolean ALLOW_LOOP_UPDATE_IN_PLACE

      Enables a specific rewrite that enables update in place for loop variables that are only read/updated via cp leftindexing.

    - #### ALLOW_CODE_MOTION

          public static boolean ALLOW_CODE_MOTION

      Enables a specific rewrite for code motion, i.e., hoisting loop invariant code out of while, for, and parfor loops.

    - #### FEDERATED_COMPILATION

          public static boolean FEDERATED_COMPILATION

      Compile federated instructions based on input federation state and privacy constraints.

    - #### PARALLEL_CP_READ_PARALLELISM_MULTIPLIER

          public static final double PARALLEL_CP_READ_PARALLELISM_MULTIPLIER

      Specifies a multiplier computing the degree of parallelism of parallel text read/write out of the available degree of parallelism. Set it to 1.0 to get a number of threads equal the number of virtual cores.
      See Also:
        Constant Field Values

    - #### PARALLEL_CP_WRITE_PARALLELISM_MULTIPLIER

          public static final double PARALLEL_CP_WRITE_PARALLELISM_MULTIPLIER

      See Also:
        Constant Field Values

    - #### ALLOW_COMBINE_FILE_INPUT_FORMAT

          public static final boolean ALLOW_COMBINE_FILE_INPUT_FORMAT

      Enables the use of CombineSequenceFileInputFormat with splitsize = 2x hdfs blocksize, if sort buffer size large enough and parallelism not hurt. This solves to issues: (1) it combines small files (depending on producers), and (2) it reduces task latency of large jobs with many tasks by factor 2.
      See Also:
        Constant Field Values

    - #### ALLOW_SCRIPT_LEVEL_COMPRESS_COMMAND

          public static final boolean ALLOW_SCRIPT_LEVEL_COMPRESS_COMMAND

      This variable allows for insertion of Compress and decompress in the dml script from the user. This is added because we want to have a way to test, and verify the correct placement of compress and decompress commands.
      See Also:
        Constant Field Values

    - #### ALLOW_COMPRESSION_REWRITE

          public static boolean ALLOW_COMPRESSION_REWRITE

      Boolean specifying if compression rewrites is allowed. This is disabled at run time if the IPA for Workload aware compression is activated.

    - #### ALLOW_TRANSITIVE_SPARK_EXEC_TYPE

          public static boolean ALLOW_TRANSITIVE_SPARK_EXEC_TYPE

      Enable transitive spark execution type selection. This refines the exec-type selection logic of unary aggregates by pushing * the unary aggregates, whose inputs are created by spark instructions, to spark execution type as well.

    - #### ASYNC_TRIGGER_RDD_OPERATIONS

          public static boolean ASYNC_TRIGGER_RDD_OPERATIONS

      Enable prefetch and broadcast. Prefetch asynchronously calls acquireReadAndRelease() to trigger a chain of spark transformations, which would would otherwise make the next instruction wait till completion. Broadcast allows asynchronously transferring the data to all the nodes.

  - ### Constructor Detail

    - #### OptimizerUtils

          public OptimizerUtils()

  - ### Method Detail

    - #### getOptLevel

          public static OptimizerUtils.OptimizationLevel getOptLevel()

    - #### isMemoryBasedOptLevel

          public static boolean isMemoryBasedOptLevel()

    - #### isOptLevel

          public static boolean isOptLevel(OptimizerUtils.OptimizationLevel level)

    - #### constructCompilerConfig

          public static CompilerConfig constructCompilerConfig(DMLConfig dmlconf)

    - #### constructCompilerConfig

          public static CompilerConfig constructCompilerConfig(CompilerConfig cconf,
                                                               DMLConfig dmlconf)

    - #### resetStaticCompilerFlags

          public static void resetStaticCompilerFlags()

    - #### getDefaultSize

          public static long getDefaultSize()

    - #### resetDefaultSize

          public static void resetDefaultSize()

    - #### getDefaultFrameSize

          public static int getDefaultFrameSize()

    - #### getLocalMemBudget

          public static double getLocalMemBudget()

      Returns memory budget (according to util factor) in bytes
      Returns:
        local memory budget

    - #### isMaxLocalParallelism

          public static boolean isMaxLocalParallelism(int k)

    - #### isTopLevelParFor

          public static boolean isTopLevelParFor()

    - #### checkSparkBroadcastMemoryBudget

          public static boolean checkSparkBroadcastMemoryBudget(double size)

    - #### checkSparkBroadcastMemoryBudget

          public static boolean checkSparkBroadcastMemoryBudget(long rlen,
                                                                long clen,
                                                                long blen,
                                                                long nnz)

    - #### checkSparkCollectMemoryBudget

          public static boolean checkSparkCollectMemoryBudget(DataCharacteristics dc,
                                                              long memPinned)

    - #### checkSparkCollectMemoryBudget

          public static boolean checkSparkCollectMemoryBudget(DataCharacteristics dc,
                                                              long memPinned,
                                                              boolean checkBP)

    - #### checkSparseBlockCSRConversion

          public static boolean checkSparseBlockCSRConversion(DataCharacteristics dcIn)

    - #### getNumReducers

          public static int getNumReducers(boolean configOnly)

      Returns the number of reducers that potentially run in parallel. This is either just the configured value (SystemDS config) or the minimum of configured value and available reduce slots.
      Parameters:
        `configOnly` - true if configured value
      Returns:
        number of reducers

    - #### getNumMappers

          public static int getNumMappers()

    - #### getDefaultExecutionMode

          public static Types.ExecMode getDefaultExecutionMode()

    - #### isSparkExecutionMode

          public static boolean isSparkExecutionMode()

    - #### isHybridExecutionMode

          public static boolean isHybridExecutionMode()

    - #### getParallelTextReadParallelism

          public static int getParallelTextReadParallelism()

      Returns the degree of parallelism used for parallel text read. This is computed as the number of virtual cores scales by the PARALLEL_READ_PARALLELISM_MULTIPLIER. If PARALLEL_READ_TEXTFORMATS is disabled, this method returns 1.
      Returns:
        degree of parallelism

    - #### getParallelBinaryReadParallelism

          public static int getParallelBinaryReadParallelism()

    - #### getParallelTextWriteParallelism

          public static int getParallelTextWriteParallelism()

      Returns the degree of parallelism used for parallel text write. This is computed as the number of virtual cores scales by the PARALLEL_WRITE_PARALLELISM_MULTIPLIER. If PARALLEL_WRITE_TEXTFORMATS is disabled, this method returns 1.
      Returns:
        degree of parallelism

    - #### getParallelBinaryWriteParallelism

          public static int getParallelBinaryWriteParallelism()

    - #### estimateSize

          public static long estimateSize(DataCharacteristics dc)

    - #### estimateSizeExactSparsity

          public static long estimateSizeExactSparsity(DataCharacteristics dc)

    - #### estimateSizeExactSparsity

          public static long estimateSizeExactSparsity(long nrows,
                                                       long ncols,
                                                       long nnz)

      Estimates the footprint (in bytes) for an in-memory representation of a matrix with dimensions=(nrows,ncols) and and number of non-zeros nnz.
      Parameters:
        `nrows` - number of rows
        `ncols` - number of cols
        `nnz` - number of non-zeros
      Returns:
        memory footprint

    - #### estimateSizeExactSparsity

          public static long estimateSizeExactSparsity(long nrows,
                                                       long ncols,
                                                       double sp)

      Estimates the footprint (in bytes) for an in-memory representation of a matrix with dimensions=(nrows,ncols) and sparsity=sp. This function can be used directly in Hops, when the actual sparsity is known i.e., `sp` is guaranteed to give worst-case estimate (e.g., Rand with a fixed sparsity). In all other cases, estimateSize() must be used so that worst-case estimates are computed, whenever applicable.
      Parameters:
        `nrows` - number of rows
        `ncols` - number of cols
        `sp` - sparsity
      Returns:
        memory footprint

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(DataCharacteristics dc)

      Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with the given matrix characteristics
      Parameters:
        `dc` - matrix characteristics
      Returns:
        memory estimate

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(DataCharacteristics dc,
                                                                  boolean outputEmptyBlocks)

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(long rlen,
                                                                  long clen,
                                                                  long blen,
                                                                  long nnz)

      Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with dimensions=(nrows,ncols) and number of non-zeros nnz.
      Parameters:
        `rlen` - number of rows
        `clen` - number of cols
        `blen` - rows/cols per block
        `nnz` - number of non-zeros
      Returns:
        memory estimate

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(long rlen,
                                                                  long clen,
                                                                  long blen,
                                                                  long nnz,
                                                                  boolean outputEmptyBlocks)

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(Hop hop)

      Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with the hops dimensions and number of non-zeros nnz.
      Parameters:
        `hop` - The hop to extract dimensions and nnz from
      Returns:
        the memory estimate

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(long rlen,
                                                                  long clen,
                                                                  long blen,
                                                                  double sp)

      Estimates the footprint (in bytes) for a partitioned in-memory representation of a matrix with dimensions=(nrows,ncols) and sparsity=sp.
      Parameters:
        `rlen` - number of rows
        `clen` - number of cols
        `blen` - rows/cols per block
        `sp` - sparsity
      Returns:
        memory estimate

    - #### estimatePartitionedSizeExactSparsity

          public static long estimatePartitionedSizeExactSparsity(long rlen,
                                                                  long clen,
                                                                  long blen,
                                                                  double sp,
                                                                  boolean outputEmptyBlocks)

    - #### estimateSize

          public static long estimateSize(long nrows,
                                          long ncols)

      Similar to estimate() except that it provides worst-case estimates when the optimization type is ROBUST.
      Parameters:
        `nrows` - number of rows
        `ncols` - number of cols
      Returns:
        memory estimate

    - #### estimateSizeEmptyBlock

          public static long estimateSizeEmptyBlock(long nrows,
                                                    long ncols)

    - #### estimateSizeTextOutput

          public static long estimateSizeTextOutput(long rows,
                                                    long cols,
                                                    long nnz,
                                                    Types.FileFormat fmt)

    - #### estimateSizeTextOutput

          public static long estimateSizeTextOutput(int[] dims,
                                                    long nnz,
                                                    Types.FileFormat fmt)

    - #### getTotalMemEstimate

          public static double getTotalMemEstimate(Hop[] in,
                                                   Hop out)

    - #### getTotalMemEstimate

          public static double getTotalMemEstimate(Hop[] in,
                                                   Hop out,
                                                   boolean denseOut)

    - #### isIndexingRangeBlockAligned

          public static boolean isIndexingRangeBlockAligned(IndexRange ixrange,
                                                            DataCharacteristics mc)

      Indicates if the given indexing range is block aligned, i.e., it does not require global aggregation of blocks.
      Parameters:
        `ixrange` - indexing range
        `mc` - matrix characteristics
      Returns:
        true if indexing range is block aligned

    - #### isIndexingRangeBlockAligned

          public static boolean isIndexingRangeBlockAligned(long rl,
                                                            long ru,
                                                            long cl,
                                                            long cu,
                                                            long blen)

      Indicates if the given indexing range is block aligned, i.e., it does not require global aggregation of blocks.
      Parameters:
        `rl` - rows lower
        `ru` - rows upper
        `cl` - cols lower
        `cu` - cols upper
        `blen` - rows/cols per block
      Returns:
        true if indexing range is block aligned

    - #### isValidCPDimensions

          public static boolean isValidCPDimensions(DataCharacteristics mc)

    - #### isValidCPDimensions

          public static boolean isValidCPDimensions(long rows,
                                                    long cols)

      Returns false if dimensions known to be invalid; other true
      Parameters:
        `rows` - number of rows
        `cols` - number of cols
      Returns:
        true if dimensions valid

    - #### isValidCPDimensions

          public static boolean isValidCPDimensions(Types.ValueType[] schema,
                                                    String[] names)

      Returns false if schema and names are not properly specified; other true Length to be > 0, and length of both to be equal.
      Parameters:
        `schema` - the schema
        `names` - the names
      Returns:
        false if schema and names are not properly specified

    - #### isValidCPMatrixSize

          public static boolean isValidCPMatrixSize(long rows,
                                                    long cols,
                                                    double sparsity)

      Determines if valid matrix size to be represented in CP data structures. Note that sparsity needs to be specified as rows*cols if unknown.
      Parameters:
        `rows` - number of rows
        `cols` - number of cols
        `sparsity` - the sparsity
      Returns:
        true if valid matrix size

    - #### exceedsCachingThreshold

          public static boolean exceedsCachingThreshold(long dim2,
                                                        double outMem)

      Indicates if the given matrix characteristics exceed the threshold for caching, i.e., the matrix should be cached.
      Parameters:
        `dim2` - dimension 2
        `outMem` - ?
      Returns:
        true if the given matrix characteristics exceed threshold

    - #### getUniqueTempFileName

          public static String getUniqueTempFileName()

      Wrapper over internal filename construction for external usage.
      Returns:
        unique temp file name

    - #### allowsToFilterEmptyBlockOutputs

          public static boolean allowsToFilterEmptyBlockOutputs(Hop hop)

    - #### getConstrainedNumThreads

          public static int getConstrainedNumThreads(int maxNumThreads)

    - #### getDefaultLogLevel

          public static org.apache.log4j.Level getDefaultLogLevel()

    - #### getMatMultNnz

          public static long getMatMultNnz(double sp1,
                                           double sp2,
                                           long m,
                                           long k,
                                           long n,
                                           boolean worstcase)

    - #### getMatMultSparsity

          public static double getMatMultSparsity(double sp1,
                                                  double sp2,
                                                  long m,
                                                  long k,
                                                  long n,
                                                  boolean worstcase)

      Estimates the result sparsity for Matrix Multiplication A %*% B.
      Parameters:
        `sp1` - sparsity of A
        `sp2` - sparsity of B
        `m` - nrow(A)
        `k` - ncol(A), nrow(B)
        `n` - ncol(B)
        `worstcase` - true if worst case
      Returns:
        the sparsity

    - #### getLeftIndexingSparsity

          public static double getLeftIndexingSparsity(long rlen1,
                                                       long clen1,
                                                       long nnz1,
                                                       long rlen2,
                                                       long clen2,
                                                       long nnz2)

    - #### isBinaryOpConditionalSparseSafe

          public static boolean isBinaryOpConditionalSparseSafe(Types.OpOp2 op)

      Determines if a given binary op is potentially conditional sparse safe.
      Parameters:
        `op` - the HOP OpOp2
      Returns:
        true if potentially conditional sparse safe

    - #### isBinaryOpConditionalSparseSafeExact

          public static boolean isBinaryOpConditionalSparseSafeExact(Types.OpOp2 op,
                                                                     LiteralOp lit)

      Determines if a given binary op with scalar literal guarantee an output sparsity which is exactly the same as its matrix input sparsity.
      Parameters:
        `op` - the HOP OpOp2
        `lit` - literal operator
      Returns:
        true if output sparsity same as matrix input sparsity

    - #### isBinaryOpSparsityConditionalSparseSafe

          public static boolean isBinaryOpSparsityConditionalSparseSafe(Types.OpOp2 op,
                                                                        LiteralOp lit)

    - #### getBinaryOpSparsityConditionalSparseSafe

          public static double getBinaryOpSparsityConditionalSparseSafe(double sp1,
                                                                        Types.OpOp2 op,
                                                                        LiteralOp lit)

    - #### getBinaryOpSparsity

          public static double getBinaryOpSparsity(double sp1,
                                                   double sp2,
                                                   Types.OpOp2 op,
                                                   boolean worstcase)

      Estimates the result sparsity for matrix-matrix binary operations (A op B)
      Parameters:
        `sp1` - sparsity of A
        `sp2` - sparsity of B
        `op` - binary operation
        `worstcase` - true if worst case
      Returns:
        result sparsity for matrix-matrix binary operations

    - #### getOuterNonZeros

          public static long getOuterNonZeros(long n1,
                                              long n2,
                                              long nnz1,
                                              long nnz2,
                                              Types.OpOp2 op)

    - #### getNnz

          public static long getNnz(long dim1,
                                    long dim2,
                                    double sp)

    - #### getSparsity

          public static double getSparsity(DataCharacteristics dc)

    - #### getSparsity

          public static double getSparsity(long dim1,
                                           long dim2,
                                           long nnz)

    - #### getSparsity

          public static double getSparsity(Hop hop)

    - #### getSparsity

          public static double getSparsity(long[] dims,
                                           long nnz)

    - #### toMB

          public static String toMB(double inB)

    - #### getNumIterations

          public static long getNumIterations(ForProgramBlock fpb,
                                              long defaultValue)

    - #### getNumIterations

          public static long getNumIterations(ForProgramBlock fpb,
                                              LocalVariableMap vars,
                                              long defaultValue)

    - #### rEvalSimpleLongExpression

          public static long rEvalSimpleLongExpression(Hop root,
                                                       HashMap<Long,Long> valMemo)

      Function to evaluate simple size expressions over literals and now/ncol. It returns the exact results of this expressions if known, otherwise Long.MAX_VALUE if unknown.
      Parameters:
        `root` - the root high-level operator
        `valMemo` - ?
      Returns:
        size expression

    - #### rEvalSimpleLongExpression

          public static long rEvalSimpleLongExpression(Hop root,
                                                       HashMap<Long,Long> valMemo,
                                                       LocalVariableMap vars)

    - #### rEvalSimpleDoubleExpression

          public static double rEvalSimpleDoubleExpression(Hop root,
                                                           HashMap<Long,Double> valMemo)

    - #### rEvalSimpleDoubleExpression

          public static double rEvalSimpleDoubleExpression(Hop root,
                                                           HashMap<Long,Double> valMemo,
                                                           LocalVariableMap vars)

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